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Double U-Net CycleGAN for 3D MR to CT image synthesis
PurposeCycleGAN and its variants are widely used in medical image synthesis, which can use unpaired data for medical image synthesis. The most...
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Lymph node detection in CT scans using modified U-Net with residual learning and 3D deep network
PurposeLymph node (LN) detection is a crucial step that complements the diagnosis and treatments involved during cancer investigations. However, the...
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High-angular resolution diffusion imaging generation using 3d u-net
PurposeTo investigate the effects on tractography of artificial intelligence-based prediction of motion-probing gradients (MPGs) in...
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The Accuracy and Radiomics Feature Effects of Multiple U-net-Based Automatic Segmentation Models for Transvaginal Ultrasound Images of Cervical Cancer
Ultrasound (US) imaging has been recognized and widely used as a screening and diagnostic imaging modality for cervical cancer all over the world....
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Skin Lesion Area Segmentation Using Attention Squeeze U-Net for Embedded Devices
Melanoma is the deadliest form of skin cancer. Early diagnosis of malignant lesions is crucial for reducing mortality. The use of deep learning...
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Hippocampus Segmentation Using U-Net Convolutional Network from Brain Magnetic Resonance Imaging (MRI)
Hippocampus is a part of the limbic system in human brain that plays an important role in forming memories and dealing with intellectual abilities....
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U-Net-based image segmentation of the whole heart and four chambers on pediatric X-ray computed tomography
This study aimed to determine whether a U-Net-based segmentation method could be used to automatically extract regions of the whole heart and...
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Automatic liver tumor segmentation used the cascade multi-scale attention architecture method based on 3D U-Net
PurposeDue to the complex structure of liver tumors and the low contrast with normal tissues make it still a challenging task to accurately segment...
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Brain tumor segmentation in MRI images using nonparametric localization and enhancement methods with U-net
Purpose:Segmentation is one of the critical steps in analyzing medical images since it provides meaningful information for the diagnosis, monitoring,...
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Nested U-Net for Segmentation of Red Lesions in Retinal Fundus Images and Sub-image Classification for Removal of False Positives
Diabetic retinopathy is a pathological change of the retina that occurs for long-term diabetes. The patients become symptomatic in advanced stages of...
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Accurate pancreas segmentation using multi-level pyramidal pooling residual U-Net with adversarial mechanism
BackgroundA novel multi-level pyramidal pooling residual U-Net with adversarial mechanism was proposed for organ segmentation from medical imaging,...
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U-Net combined with multi-scale attention mechanism for liver segmentation in CT images
BackgroundThe liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with...
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UViT-Seg: An Efficient ViT and U-Net-Based Framework for Accurate Colorectal Polyp Segmentation in Colonoscopy and WCE Images
Colorectal cancer (CRC) stands out as one of the most prevalent global cancers. The accurate localization of colorectal polyps in endoscopy images is...
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Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images
BackgroundThe 3D U-Net model has been proved to perform well in the automatic organ segmentation. The aim of this study is to evaluate the...
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Effects of sample size and data augmentation on U-Net-based automatic segmentation of various organs
Deep learning has demonstrated high efficacy for automatic segmentation in contour delineation, which is crucial in radiation therapy planning....
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Segmenting Skin Biopsy Images with Coarse and Sparse Annotations using U-Net
The number of melanoma diagnoses has increased dramatically over the past three decades, outpacing almost all other cancers. Nearly 1 in 4 skin...
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Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model
To develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model...
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A study of generalization and compatibility performance of 3D U-Net segmentation on multiple heterogeneous liver CT datasets
BackgroundMost existing algorithms have been focused on the segmentation from several public Liver CT datasets scanned regularly (no pneumoperitoneum...
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COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet
BackgroundCurrently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible...
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DUDA-Net: a double U-shaped dilated attention network for automatic infection area segmentation in COVID-19 lung CT images
PurposeThe global health crisis caused by coronavirus disease 2019 (COVID-19) is a common threat facing all humankind. In the process of diagnosing...
